A New Model for Bank Stress Tests

In recent years, policymakers have struggled with the question of how to prevent bank failures. The Dodd-Frank Act offers one answer, calling for stress tests that examine through economic models how banks of a certain size would react to a bad turn of economic events, such as negative interest rates. The 2016 stress tests, for example, required banks to consider their preparedness for negative U.S. short-term Treasury rates and major losses to their corporate and commercial real estate lending portfolios.[1] A failed stress test raises red flags about whether a bank has enough capital to stay solvent in a crisis.

Although stress testing is not new, what is new is the Federal Reserve’s role in setting bad case scenarios and requiring banks to use them in their stress tests, the results of which must be reported annually. The Board of Governors must provide at least three sets of conditions under which the evaluation shall be conducted: baseline, adverse, and severely adverse.

In 2010, the Federal Reserve also initiated the annual Comprehensive Capital Analysis and Review (CCAR) exercise, which involves quantitative stress tests and a qualitative assessment of the largest bank holding companies’ capital planning practices, requiring the banks to submit their detailed capital plans. CCAR is separate from the Dodd-Frank stress tests, affecting only the largest banks and becoming a main component of the Federal Reserve System’s supervisory program for them.[2]

In stress testing, modelers must measure both market risk and credit risk. Market risk is the possibility that the banks will lose money on trading stocks and bonds, while credit risk is the possibility that their customers will default on their loans. An additional risk is that the model does not accurately reflect all possible outcomes, which could lead to a failed stress test.

There are currently no guiding models for stress testing. While the Federal Reserve stress testing methods have not been publicly disclosed, the banks have been more transparent in their stress testing. They have not used the Bayesian model in their stress tests, which is a statistical approach in which prior results are used to calculate the probabilities of future events.

There are significant advantages to a Bayesian model in the context of stress testing. For instance, the negative interest rates scenarios are a great example of the utility of the Bayesian method. Given the importance of stress testing to bank regulation in the current legal framework, it is worth considering the Bayesian model in stress testing.

In our forthcoming article, we build a Bayesian model that takes into account prior inputs. We use two sources of data: the hypothetical economic scenarios released by the Federal Reserve annually and the consolidated financial statements of banks, which detail credit losses by type of loan.

A Bayesian model helps incorporate data from the experience and judgment of experts. Indeed, expert opinion is a form of data, soft as it may be. For example, models might be missing input from loan officers, even when this input is helpful – a loan officer issuing mortgages for 30 years might have a valuable perspective. A Bayesian methodology allows incorporation of these views into the model. In this regard, we note that the careful formation of priors is of paramount importance in Bayesian analysis and should be tailored to the modeling context.

In the context of the Bayesian model described in our article, the priors would be the previous Federal Reserve adverse scenarios. They are useful because of the financial industry’s view that the Federal Reserve adapts its scenarios each year to stress certain portfolios, but remains consistent with its scenarios from previous years.

Our article concludes that failure to consider these prior scenarios could underestimate by as much as 25 percent a bank’s loan losses in an adverse economic scenario. This could be the difference between passing and failing a stress test.

Stress testing is an evolving field, and banks are constantly trying to forecast potential losses in a future recession so they can manage their capital effectively. Therefore, more innovations in modeling credit risk will lead to more accurate models for stress testing.

This post comes to us from Professor Margaret Ryznar at Indiana University’s McKinney School of Law and Michael Jacobs, Jr., a principal director at Accenture Consulting. It is based on their recent paper, “Implementing Dodd-Frank Act Stress Testing,” availablehere.